Various reasons may exist for using fertilizer efficiently. For example, as fertilizer prices rise as a result of increasing energy and/or mineral costs, farming operations (e.g., farmers) may be incentivized to use fertilizer as efficiently as possible, such as, for example, by using only what fertilizer is needed to fertilize agricultural fields, in order to minimize fertilizer expenses. Meanwhile, using fertilizer efficiently offers more than just economic value. Groundwater beneath many farmed regions throughout the United States (e.g., the central United States) has become polluted with quantities of nitrate far exceeding safe drinking water standards. Over-fertilization of agricultural fields may cause such nitrate pollution of groundwater as a result of fertilizer leaching with precipitation or irrigation through and below crop root zones.
Because soil across agricultural fields may vary, one or more areas of an agricultural field may have different growing conditions (e.g., chemical and/or physical conditions) than one or more other areas of the agricultural field. For example, one or more areas of the agricultural field may have less favorable growing conditions than one or more other areas of the agricultural field. As a result, one or more areas of an agricultural field may have different fertilizer needs than one or more other areas of the agricultural field. Accordingly, various conventional systems and/or methods for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions have been developed with the intent of helping farming operations use fertilizer more efficiently.
Some conventional systems for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions use localized sensing to determine a quantity and/or type of fertilizer to provide to the different areas. For example, these conventional systems may sample the soil of an agricultural field at multiple separate soil sampling locations (e.g., at least one soil sampling location per area) to determine a quantity and/or type of fertilizer to provide to the different areas. Unfortunately, in practice, the costs of implementing localized sensing may negate some or all of the savings that might be realized from reduced fertilizer expenses and/or increased income from enhanced yields that may result from using the localized sensing. For example, the equipment infrastructure needed to sample soil at the multiple separate soil sampling locations may be expensive and/or difficult to install, operate, and/or maintain. Meanwhile, because the accuracy of localized sensing depends on the quantity of soil sampling locations analyzed, attempts to reduce the costs of implementing localized sensing by limiting a quantity of soil sampling locations would result in a corresponding decrease in the accuracy, and consequently the effectiveness of implementing localized sensing.
Further conventional systems for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions use remote sensing (e.g., aerial or spatial measurements) to determine a quantity and/or type of fertilizer to provide to the different areas. For example, these conventional systems may apply a graphical indicator such as a vegetation index to analyze measurements obtained by remote sensing to determine a quantity and/or type of fertilizer to provide to the different areas.
An exemplary vegetation index used by conventional systems implementing remote sensing may include a normalized difference vegetation index (NDVI). The NDVI provides a ratio indicative of the density of vegetation using measurements of the visible red and near-infrared light reflected by vegetation (i.e., reflectance). Unfortunately, the accuracy of using the NDVI as a graphical indicator from one period of time (e.g., a minute, an hour, a day, a week, a month, a year, etc.) to the next, such as, for example, in expressing growth curves or in comparing agricultural fields over periods of time may be compromised by the effects of atmospheric aerosols and/or soil background. For example, aerosols can impact reflectance by scattering and/or attenuating light, resulting in an NDVI value that inaccurately represents the actual density of vegetation present. Meanwhile, because the NDVI of bare soil is typically greater than zero, and sometimes considerably greater than zero, the NDVI value of soil background can suggest vegetation is present even where no vegetation is present. Consequently, many conventional systems using the NDVI as a vegetation index have been unreliable for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions because the NDVI only reliably permits comparison and calculation of vegetation across agricultural fields at contemporaneous moments in time and does not reliably permit comparison of vegetation between or over periods of time.
Some conventional systems that implement remote sensing for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions have attempted to address the shortcomings of using NDVI as a vegetation index. However, these conventional systems still have their own shortcomings.
For example, one exemplary conventional system maps the nitrogen required by areas of an agricultural field to prepare for application of nitrogen fertilizer to the agricultural field. This conventional system uses remote sensing data to prescribe and deliver nitrogen for the agricultural field. However, in addition to using the remote sensing data, this conventional system relies on ground-based measurements of reflectance to calibrate an NDVI to remove atmospheric aerosol effects, and thus, is poorly scalable and suffers from similar shortcomings to those of localized sensing as discussed above. In particular, many farming operations lack the proper equipment and/or scientific know-how to calibrate and use the NDVI using ground-based measurements of reflectance. Further, such equipment and/or training is expensive and may be cost prohibitive for many farming operations. Meanwhile, this conventional system does nothing to account for soil background effects on the NDVI.
Another exemplary conventional system implementing an NDVI with remote sensing attempts to eliminate atmospherically-induced error in the NDVI by measuring the NDVI of vegetation just over the canopy of the vegetation. In particular, this conventional system uses sensors mounted on farm equipment, such as, for example, on tractor-dragged equipment or on the boom of center-pivot irrigation systems, to obtain remote sensing measurements. Although avoiding the error-inducing effects of atmospheric aerosols on the NDVI, this conventional system suffers from other problems that still prevent this conventional system from addressing the shortcomings of using NDVI as a vegetation index. For example, the close proximity to the ground of the sensors mounted on farm equipment may cause inconsistencies in remote sensing measurements taken by the sensors throughout the day due to changes in lighting resulting from changes in the position of the sun, cloud cover, haze, and/or twilight. These problems may become particularly significant where an agricultural field is sufficiently large such that fertilizing the agricultural field requires extended periods of time to complete. Even assuming clear skies, remote sensing measurements taken by the sensors of this conventional system may only be consistent for a few hours when the sun is near zenith. Meanwhile, sensors mounted on center pivot irrigation systems may require many days to make full circles and may necessitate nighttime measurements that this conventional system does not address.
Yet another exemplary conventional system implementing an NDVI with remote sensing uses an automated correction routine to compensate for atmospheric influences on the NDVI applied to remote sensing data. However, this conventional system does not correct the effects of soil background on the NDVI that may result in erroneous values of the NDVI applied to remote sensing measurements for agricultural fields having little or no vegetation.
By failing to account for the effects of soil background, this conventional system may be unable to reliably analyze early season remote sensing measurements (e.g., when crops are in early stages of development and/or before the canopy of the crops closes) and yet accurately analyzing early season remote sensing measurements may be critical to determining the timing of crop development. For example, values of the NDVI may be highly influenced by soil reflectance before the canopy of crops closes and remote sensing measurements taken during the period of time before the canopy of crops closes may be important for predicting a crop's growth stage. While this conventional system may use bare soil reflectance to map soil brightness, normalized from zero to one, soil brightness is not a correction for soil background and is not an appropriate tool to judge soil properties or fertility. Surface soil water content, along with the presence of crop residue in the form of leaves and stems left-over from the prior season, control the soil brightness perceived by remote sensing. Neither surface soil water content nor exposed crop residues are well correlated to agricultural soil properties or fertility.
Meanwhile, this conventional system is unreliable for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions because the remote sensing analysis implemented by this conventional system may be insufficiently sensitive to evaluate a spatial distribution of crops in an agricultural field, such as, for example, when a leaf area index (LAI) of the crops is within an upper third of a potential LAI of the crops. LAI is a dimensionless measurement of the average number of leaf layers covering the ground across an agricultural field. For example, this conventional system uses a relationship between NDVI and LAI to map the LAI of the crops of an agricultural field. However, the relationship between NDVI and LAI for cultivated crops saturates at values of LAI of approximately four or five at which point NDVI no longer provides a reliable measure of LAI, and yet values of LAI exceeding seven are common for cultivated crops.
Further, this conventional system is unreliable for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions because the conventional system does not determine when to obtain remote sensing data to analyze yield patterns of an agricultural field. The timing for a crop to display a spatial-yield pattern may be important for accurately prescribing and delivering fertilizer over an agricultural field. The conventional system only provides for obtaining remote sensing data during a particular window of time (e.g., a crop's last vegetative state). Because yield patterns of an agricultural field may vary, reliance on yield patterns during a particular window of time may cause an erroneous understanding of spatial yield and, therefore, any fertilizer prescription determined from it may be erroneous as well.
Accordingly, there is a need for improved systems and methods for prescribing and delivering fertilizer over areas of agricultural fields having varying growing conditions, such as, for example, so that each area of the agricultural fields receives only a quantity of fertilizer that is sufficient to permit that area to reach its particular yield potential. Further, such systems and methods are needed that can be implemented across farmed regions that may include multiple agricultural fields, such as, for example, so that the systems and methods can be implemented for the benefit of multiple farming operations.
For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the invention. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present invention. The same reference numerals in different figures denote the same elements.